Stochastic thinning of nearest-neighbor pairwise composite likelihoods achieves 10-30x speedup over Vecchia approximation on multi-million-point spatial datasets with modest efficiency loss, concentrated on smoothness estimation.
A Unifying Framework for Parallel and Distributed Processing in R using Futures
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abstract
A future is a programming construct designed for concurrent and asynchronous evaluation of code, making it particularly useful for parallel processing. The future package implements the Future API for programming with futures in R. This minimal API provides sufficient constructs for implementing parallel versions of well-established, high-level map-reduce APIs. The future ecosystem supports exception handling, output and condition relaying, parallel random number generation, and automatic identification of globals lowering the threshold to parallelize code. The Future API bridges parallel frontends with parallel backends following the philosophy that end-users are the ones who choose the parallel backend while the developer focuses on what to parallelize. A variety of backends exist and third-party contributions meeting the specifications, which ensure that the same code works on all backends, are automatically supported. The future framework solves several problems not addressed by other parallel frameworks in R.
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Fast Stochastic Nearest Neighbor Pairwise Composite Likelihood for Massive Spatial Datasets
Stochastic thinning of nearest-neighbor pairwise composite likelihoods achieves 10-30x speedup over Vecchia approximation on multi-million-point spatial datasets with modest efficiency loss, concentrated on smoothness estimation.